Exploring Vector Databases

Amid the AI revolution, diverse AI models like large language and generative AI models have come into the limelight. These novel AI models require efficient data processing, achievable using vector embeddings. By providing semantic information to the AI models, they gain a better understanding and can perform complex tasks efficiently. The embeddings are generated by AI models that contain numerous attributes or features, posing a management challenge. These attributes signify various data features fundamental to apprehending patterns, architectures, or understanding any underlying interconnections among the attributes. To manage such data types, a specialized database is indispensable.

The vector database allows data to be stored as high-dimensional vectors, mathematically representing various features and attributes within the data. Data can be stored in vectors within specific dimensions tailored to that data's complexity. These vectors are produced using specialized procedures to transform raw data from various sources, such as texts, audio, or images, into the intended information.

Traditional scalar databases are inadequate to handle the intricacy and magnitude of these data types, posing a challenge in acquiring real-time insights. Vector databases excel in managing these challenges of vector data while empowering AI models with performance, scalability, and flexibility.

The vector database can quickly retrieve similar objects to a specific query due to pre-computations. This is performed using the concept of the approximate nearest neighbor search that involves the implementation of algorithms for indexing and calculating the similarities.

In this blog, we will go over some of the popular vector databases.

Most Popular Vector Databases and Libraries

Pretrained AI models have driven up the demand for vector databases in recent years. The databases are recognized for broadening the capabilities when fine-tuning the pre-trained models. However, its scope is not limited to pre-trained models only. Several organizations have prioritized building their own vector databases, and the most popular ones are discussed in the next section.

Milvus

• Milvus is a popular open-source vector database among data scientists. The primary advantage of Milvus is the provision for both vector indexing and querying. This procedure enables faster retrieval of similar vectors and is most effective when working with large-scale datasets.

Key features:

• One of the standout features of the database is its ability to facilitate easy large-scale similarity search. There are also provisions for SDKs in various languages.
• Sophisticate indexing algorithms of the database enhance the speed of information retrieval.
• Milvus database has an approach that is systemic towards cloud nativity by segregating computation and storage.
• The ability of the database to effortlessly integrate widely used frameworks such as PyTorch, TensorFlow, OpenAI, Transformers and more is another crucial factor to consider when working with machine learning workflows.
• Because of the distributed nature and high throughout, this database is an excellent option for dealing with large-scale vector data.
• Several features are available for various data types, including improved vector search, attribute filtering, UDF support, flexible consistency level, and time travel.

Weaviate

• Weaviate: Weviate is another robust open-source vector database allowing users to self-host and store data and vector embeddings. Some of the prominent features of the database are:

Key features:

• Users can store data objects and vector embeddings their machine-learning models generate.
• The option to bring custom vectors or use available vectorization modules to index multiple data objects for searching is highly effective.
• The database facilitates the integration of search methods, such as keyword-based and vector-based search to enhance the search experience.
• LLM models like ChatGPT-3 provide support for generative search to enhance search outcomes.
• Weaviate guarantees seamless integration with frameworks such as OpenAI, Transformers, Deepset, Cohere, and WPSolr.

Pinecone

The Pinecone database is a vector database hosted in the cloud. The database enables users to build and deploy machine learning applications efficiently. However, this database is not open-source. Pinecone’s popularity among developers can be attributed to its straightforward and intuitive interface. The database boasts several key features such as:

Key features:

• Pinecone makes it possible to build applications that are both faster and more accurate, thanks to its proprietary algorithms and well-optimized indexes.
• Multiple vectors can be queried in real-time with low latency.
• This platform offers live index updates, filters, and hybrid search support.
• Pinecone also supports vector embeddings, involving dense vectors of constant dimensions. Likewise, sparse vector embedding is rendered for vectors of higher dimensions.
• The enhanced sparse-dense support offers greater flexibility regardless of the model or data type.
• The users can use the database's added capabilities for multimodal search, boosting and managing widely distributed vectors to yield better search outcomes.
• Pinecone offers a REST API or Python SDK which is user-friendly and can be implemented for hybrid search.
• By utilizing cloud environments like Google Colab, SageMaker Notebook, or EC2, the database ensures scalability while minimizing high query latency.

Elasticsearch

Elasticsearch is a distributed RESTful search and analytics engine providing support for various data types and among them is vector fields that store dense vectors. The platform has undergone modifications with new features that allow for indexing vectors into specialized data structures. Additionally, it now supports the use of natural language processing with vector fields.

Key features:

• Elasticsearch enables the execution and merging of diverse searches such as structured, unstructured, geo, and others.
• Analysis of data on a large scale is possible with aggregation to uncover various trends and patterns.
• Rapid results are guaranteed irrespective of multiple iterations.
• Inverted indices and finite state transducers are implemented for full-text querying support.
• This platform offers extensive scalability and automated distribution of indices and queries across the clusters.
• By ranking search results, it is possible to customize how they are displayed to users.
• Elasticsearch permits the storage of data either locally or remotely.
• There are additional features that increase functionality such as log and infrastructure monitoring, application performance insights, endpoint security, enterprise search for various use cases, real-time location data identification, and automated threat detection.

SideNote: Elasticsearch introduced vector search from version 7.3 onwards. However, AWS Open search went tried to do its own thing so as of now vector search in AWS Opensearch works differently than elasticsearch.

Chroma

• Chroma: Chroma is yet another well-known open-source embedding database platform that is quite popular. The database has abundant features and is highly appropriate for developing extensive language models. The platform presents flexibility and easily scalable solutions for searching, filtering, and high-dimensional vector data retrieval. Among the top features of this platform are:

Key features:

• Deployment provisioning on both cloud and on-premise environments.
• The platform provides support for various data types and formats.
• It is a compelling database most effective for audio data analysis and is commonly utilized for developing music recommendation systems or audio search engines.
• Integrations are available with LangChain, OpenAI, and LlamaIndex.
• The flexibility to choose a coding language like Java or Python is present in the platform.
• There is a provision for both searching and analysis.
• The platform allows for the embedding of both documents and queries.
• The embedding can be stored in conjunction with the metadata.

• Qdrant: Qdrant is a practical and versatile vector database. There are data management and analysis options, and it is highly used for similarity-based suggestions, anomaly detection, and image or text searches.

Key features:

• Deployment of APIs to perform the nearest high-dimensional vector search and generate a library in a programming language.
• Python and other languages are supported to enhance functionality.
• Custom modifications are possible for algorithms such as HNSW for approximate nearest neighbor search.
• Search filters can be implemented without impacting the results.
• Vector payloads accommodate diverse data types and query conditions, including other functionalities like string matching, numerical range selection, and geo-locations.
• The platform offers exceptional scalability.
• Hardware builds are accessible and features such as dynamic query planning and payload data indexing guarantee the most effective utilization of resources.

There are multiple vector databases, but not all of them are created equal. Each platform has unique features, so take it would be best to take a glance at some of the aspects to contemplate before finalizing your vector database.

Tabular comparison

Vector Database	Open-Source	Licensing	Development Language	Supported Index	Approximate Nearest Neighbor Support	Benchmark (according to jina.ai report: Min.1 million data with 32 max.connections)
Milvus	Yes	Apache License 2.0	Python, Go, C++	HNSW, IVF PQ, RHNSW FLAT, RHNSW SQ	Yes	NA
Weaviate	Yes	BSD	C++, Go	HNSW, NGT, ANNOY	Yes	Recall:0.99 Finding by vector: 6.64 (ms) Read: 2.64 (ms) Update: 5.91(ms) Delete: 22.22 (ms)
Pinecone	No	Proprietary (No sublicensing)	Rust	Proprietary	Yes	NA
Elasticsearch	Yes	SSPL/ Elastic License	Java	HNSW	Yes	Recall:0.99 Finding by vector: 11.55 (ms) Read: 13.37 (ms) Update: 50.19(ms) Delete: 58.59 (ms)
Chroma	Yes	Apache License 2.0	Python	HNSW, IVFADC, IVFPQ	Yes	NA
Qdrant	Yes	Apache License 2.0	Rust	Self-hosted and managed	Yes	Recall:0.99 Finding by vector: 4.42 (ms) Read: 1.48 (ms) Update: 1.67(ms) Delete: 3.83 (ms)

Conclusion

Vector databases have become increasingly prevalent as a cutting-edge solution to advance search functionality. These databases offer significant benefits for data analysis, especially in cases where identifying similarities is critical. Vector databases are used in various applications today, including search engines, recommendation systems, image and video analysis systems, and AI-enabled anomaly detection systems. Incorporating these databases into real-world settings has allowed developers and researchers to maximize AI's potential and revolutionize how data is handled.

However, when selecting a vector database for your business, evaluating scalability, performance, integration, security, and durability aspects is crucial to find the ideal database.

My opinion

I might be biased but I felt #milvus seems to be my favorite of all of them due to the open-source nature and industry-wide acceptance of it.
For prototyping, I found #chroma to be an excellent candidate as it is in-memory and has simple easy-to-use APIs.